scholarly journals Cooperative trading of a price-maker wind power producer: A data-driven approach considering uncertainty

2021 ◽  
Author(s):  
Rizwan Qureshi ◽  
Saddam Aziz ◽  
Siqi Bu ◽  
sadiq ahmad ◽  
Rongquan Zhang ◽  
...  
Keyword(s):  
Wind Power ◽  
Market Price ◽  
Estimation Method ◽  
Price Uncertainty ◽  
Profit Distribution ◽  
Moment Estimation ◽  
Improved Firefly Algorithm ◽  
Data Driven Approach ◽  
Nonlinear Features ◽  
Cooperative Power

This paper presents a novel framework for cooperative trading in a price-maker wind power producer, that participates in the short-term electricity balance markets. In this framework, market price uncertainty is first modeled using a price uncertainty predictor, consisting of ridge regression (RR), nonpooling convolutional neural network (NPCNN), and linear quantile regression (LQR). RR is employed to select the correlated features to the corresponding forecast day, NPCNN is employed to extract the nonlinear features, and LQR is employed to estimate the price uncertainty. Then, an improved firefly algorithm (IFA) is proposed to solve the optimization problem. IFA uses the adaptive moment estimation method to improve the convergence speed and search for the global solution. Finally, the Shapley value is employed for the profit distribution of cooperative power producers. Illustrative examples show the effectiveness of the proposed framework and optimization model

scholarly journals Cooperative trading of a price-maker wind power producer: A data-driven approach considering uncertainty

2021 ◽  
Author(s):  
Rizwan Qureshi ◽  
Saddam Aziz ◽  
Siqi Bu ◽  
sadiq ahmad ◽  
Rongquan Zhang ◽  
...  
Keyword(s):  
Wind Power ◽  
Market Price ◽  
Estimation Method ◽  
Price Uncertainty ◽  
Profit Distribution ◽  
Moment Estimation ◽  
Improved Firefly Algorithm ◽  
Data Driven Approach ◽  
Nonlinear Features ◽  
Cooperative Power

This paper presents a novel framework for cooperative trading in a price-maker wind power producer, that participates in the short-term electricity balance markets. In this framework, market price uncertainty is first modeled using a price uncertainty predictor, consisting of ridge regression (RR), nonpooling convolutional neural network (NPCNN), and linear quantile regression (LQR). RR is employed to select the correlated features to the corresponding forecast day, NPCNN is employed to extract the nonlinear features, and LQR is employed to estimate the price uncertainty. Then, an improved firefly algorithm (IFA) is proposed to solve the optimization problem. IFA uses the adaptive moment estimation method to improve the convergence speed and search for the global solution. Finally, the Shapley value is employed for the profit distribution of cooperative power producers. Illustrative examples show the effectiveness of the proposed framework and optimization model

The Optimal Bidding Strategy Based on Multistage Risk for Wind Power Supplier

2013 ◽  
Vol 805-806 ◽  
pp. 327-333
Author(s):  
Tao Ye ◽  
Xie Lei ◽  
Yi Yang
Keyword(s):  
Wind Power ◽  
Value At Risk ◽  
Economic Benefit ◽  
Market Price ◽  
Estimation Method ◽  
Conditional Value At Risk ◽  
Bidding Strategy ◽  
Individual Risk ◽  
Calculation Results ◽  
The Impact

In order to improve the ability of grid-connected wind power supplier to predict and manage risk, and analyze the economic benefit of wind power supplier, a model of bidding strategy for grid-connected wind power supplier is established based on the risk measurements indicator of the Conditional Value-at-Risk (CVaR). The model considering the trade-off problem between risks and benefits comprehensively which includes three risk factors as: fluctuated market price, uncertain load demand and random wind power output. By using the kernel density estimation method (KDE) on output forecast, this optimal model obtains bidding strategy and economic benefit of grid-connected wind power supplier under the impact of multistage risk in different risk preferences and compares the results with the impact of individual risk fluctuation.The calculation results show the validity of the proposed method.

Tracks

2002 ◽  
Vol 17 (3) ◽  
pp. 258-272 ◽  
Author(s):  
Laurence Blose ◽  
Marinus DeBruine ◽  
Parvez Sopariwala
Keyword(s):  
Stock Market ◽  
Market Price ◽  
Estimation Method ◽  
Economic Loss ◽  
General Motors ◽  
Management Literature ◽  
Recent Developments ◽  
Unused Capacity ◽  
June July ◽  
The Cost

The General Motors/UAW strike in June-July 1998 involved a 54-day work stoppage. General Motors and automobile analysts have attempted to quantify the loss from this strike and have come up with varied numbers. The stock market analysts' reactions to the strike, and consequently, the stock market price reaction, vary depending on which estimation method is employed. There is no real consensus among these various sources because their “economic loss” estimates rely on predictions of how many of the vehicles could not be produced and sold as a result of the strike (or recovered in future quarters). Using recent developments in the cost management literature, we determine the cost of unused capacity or the “accounting loss” suffered by General Motors during the second and third quarters of 1998. We show that General Motors may have lost about $2,332.7 million after taxes (excluding Delphi) due to the strike and an additional $1,313.8 million after taxes (excluding Delphi) due to capacity unused for other reasons. More importantly, such “accounting losses,” to the extent they are not strike-related, are expected to continue each year unless market conditions improve or the company takes action to reduce its capacity.

Using data-driven approach for wind power prediction: A comparative study

2016 ◽  
Vol 118 ◽  
pp. 193-203 ◽  
Author(s):  
Ehsan Taslimi Renani ◽  
Mohamad Fathi Mohamad Elias ◽  
Nasrudin Abd. Rahim
Keyword(s):  
Comparative Study ◽  
Wind Power ◽  
Data Driven ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Data Driven Approach ◽  
Using Data

A Spectral Moment Estimation Method for Wind Profile Radar

2011 ◽  
Vol 179-180 ◽  
pp. 740-745
Author(s):  
Jian Xin Hui ◽  
Lei Wu ◽  
Yu Chun Gao ◽  
Jie Zhou
Keyword(s):  
Spectral Data ◽  
Wind Profile ◽  
Simulation Analysis ◽  
Estimation Method ◽  
Power Spectral Analysis ◽  
Radar Data ◽  
Likelihood Method ◽  
Radar Detection ◽  
Detection Range ◽  
Moment Estimation

Spectrum in the wind profile radar data processing, radar detection of low-level spectral data from the library there are usually clutter, intermittent clutter, clutter and atmospheric echoes magnetic mixed overlap situation. In order to effectively restrain and remove clutter and increase the wind profile radar detection range and accuracy, must be on the air back to the effective spectrum of the spectral moments estimation. Based on the wind profile radar Doppler echo power spectral analysis, maximum likelihood method based on estimated spectral data of radar echo spectrum method using MATLAB simulation analysis , compared with the conventional method of analysis to verify the feasibility and effectiveness of the algorithm, also, try the algorithm is applied to the complexity of the weather with a strong interference case of precipitation particles; Data analysis showed that the actual detection, from the library in the lower spectrum moment estimation has been improved significantly.

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